Overview

Dataset statistics

Number of variables34
Number of observations2940
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory781.1 KiB
Average record size in memory272.0 B

Variable types

Numeric15
Boolean3
Categorical16

Alerts

Over18 has constant value "True" Constant
StandardHours has constant value "80" Constant
Age is highly correlated with TotalWorkingYearsHigh correlation
JobLevel is highly correlated with MonthlyIncome and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
Age is highly correlated with JobLevel and 1 other fieldsHigh correlation
JobLevel is highly correlated with Age and 3 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 2 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly correlated with JobLevel and 5 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
Age is highly correlated with TotalWorkingYearsHigh correlation
JobLevel is highly correlated with MonthlyIncome and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly correlated with TotalWorkingYears and 2 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
Education is highly correlated with Over18 and 1 other fieldsHigh correlation
JobInvolvement is highly correlated with Over18 and 1 other fieldsHigh correlation
BusinessTravel is highly correlated with Over18 and 1 other fieldsHigh correlation
Department is highly correlated with JobRole and 3 other fieldsHigh correlation
JobRole is highly correlated with Department and 3 other fieldsHigh correlation
StockOptionLevel is highly correlated with Over18 and 2 other fieldsHigh correlation
OverTime is highly correlated with Over18 and 1 other fieldsHigh correlation
Over18 is highly correlated with Education and 17 other fieldsHigh correlation
StandardHours is highly correlated with Education and 17 other fieldsHigh correlation
PerformanceRating is highly correlated with Over18 and 1 other fieldsHigh correlation
WorkLifeBalance is highly correlated with Over18 and 1 other fieldsHigh correlation
Gender is highly correlated with Over18 and 1 other fieldsHigh correlation
EducationField is highly correlated with Department and 2 other fieldsHigh correlation
MaritalStatus is highly correlated with StockOptionLevel and 2 other fieldsHigh correlation
RelationshipSatisfaction is highly correlated with Over18 and 1 other fieldsHigh correlation
JobLevel is highly correlated with JobRole and 2 other fieldsHigh correlation
Attrition is highly correlated with Over18 and 1 other fieldsHigh correlation
EnvironmentSatisfaction is highly correlated with Over18 and 1 other fieldsHigh correlation
JobSatisfaction is highly correlated with Over18 and 1 other fieldsHigh correlation
Age is highly correlated with JobLevel and 3 other fieldsHigh correlation
Department is highly correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly correlated with Department and 1 other fieldsHigh correlation
JobLevel is highly correlated with Age and 6 other fieldsHigh correlation
JobRole is highly correlated with Department and 4 other fieldsHigh correlation
MaritalStatus is highly correlated with StockOptionLevelHigh correlation
MonthlyIncome is highly correlated with Age and 4 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly correlated with MaritalStatusHigh correlation
TotalWorkingYears is highly correlated with Age and 7 other fieldsHigh correlation
YearsAtCompany is highly correlated with Age and 6 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with JobLevel and 4 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with JobLevel and 4 other fieldsHigh correlation
EmployeeNumber is uniformly distributed Uniform
EmployeeNumber has unique values Unique
NumCompaniesWorked has 394 (13.4%) zeros Zeros
TrainingTimesLastYear has 108 (3.7%) zeros Zeros
YearsAtCompany has 88 (3.0%) zeros Zeros
YearsInCurrentRole has 488 (16.6%) zeros Zeros
YearsSinceLastPromotion has 1162 (39.5%) zeros Zeros
YearsWithCurrManager has 526 (17.9%) zeros Zeros

Reproduction

Analysis started2022-02-28 03:10:36.245378
Analysis finished2022-02-28 03:11:23.466500
Duration47.22 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

EmployeeNumber
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct2940
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1470.5
Minimum1
Maximum2940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:23.582431image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile147.95
Q1735.75
median1470.5
Q32205.25
95-th percentile2793.05
Maximum2940
Range2939
Interquartile range (IQR)1469.5

Descriptive statistics

Standard deviation848.849221
Coefficient of variation (CV)0.5772521054
Kurtosis-1.2
Mean1470.5
Median Absolute Deviation (MAD)735
Skewness0
Sum4323270
Variance720545
MonotonicityStrictly increasing
2022-02-27T19:11:23.752518image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
19541
 
< 0.1%
19561
 
< 0.1%
19571
 
< 0.1%
19581
 
< 0.1%
19591
 
< 0.1%
19601
 
< 0.1%
19611
 
< 0.1%
19621
 
< 0.1%
19631
 
< 0.1%
Other values (2930)2930
99.7%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
29401
< 0.1%
29391
< 0.1%
29381
< 0.1%
29371
< 0.1%
29361
< 0.1%
29351
< 0.1%
29341
< 0.1%
29331
< 0.1%
29321
< 0.1%
29311
< 0.1%

Attrition
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
False
2466 
True
474 
ValueCountFrequency (%)
False2466
83.9%
True474
 
16.1%
2022-02-27T19:11:23.867595image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92380952
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:23.970934image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.133819193
Coefficient of variation (CV)0.2473693617
Kurtosis-0.4054998352
Mean36.92380952
Median Absolute Deviation (MAD)6
Skewness0.4130752441
Sum108556
Variance83.42665306
MonotonicityNot monotonic
2022-02-27T19:11:24.156948image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35156
 
5.3%
34154
 
5.2%
36138
 
4.7%
31138
 
4.7%
29136
 
4.6%
32122
 
4.1%
30120
 
4.1%
33116
 
3.9%
38116
 
3.9%
40114
 
3.9%
Other values (33)1630
55.4%
ValueCountFrequency (%)
1816
 
0.5%
1918
 
0.6%
2022
 
0.7%
2126
 
0.9%
2232
 
1.1%
2328
 
1.0%
2452
1.8%
2552
1.8%
2678
2.7%
2796
3.3%
ValueCountFrequency (%)
6010
 
0.3%
5920
0.7%
5828
1.0%
578
 
0.3%
5628
1.0%
5544
1.5%
5436
1.2%
5338
1.3%
5236
1.2%
5138
1.3%

BusinessTravel
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Travel_Rarely
2086 
Travel_Frequently
554 
Non-Travel
300 

Length

Max length17
Median length13
Mean length13.44761905
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Rarely
4th rowTravel_Frequently
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely2086
71.0%
Travel_Frequently554
 
18.8%
Non-Travel300
 
10.2%

Length

2022-02-27T19:11:24.343698image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:24.456242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely2086
71.0%
travel_frequently554
 
18.8%
non-travel300
 
10.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DailyRate
Real number (ℝ≥0)

Distinct886
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.4857143
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:24.565916image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile164
Q1465
median802
Q31157
95-th percentile1425
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.4404468
Coefficient of variation (CV)0.5027384782
Kurtosis-1.203817139
Mean802.4857143
Median Absolute Deviation (MAD)344
Skewness-0.003516771484
Sum2359308
Variance162764.1941
MonotonicityNot monotonic
2022-02-27T19:11:24.740029image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69112
 
0.4%
40810
 
0.3%
53010
 
0.3%
132910
 
0.3%
108210
 
0.3%
32910
 
0.3%
8298
 
0.3%
14698
 
0.3%
2678
 
0.3%
2178
 
0.3%
Other values (876)2846
96.8%
ValueCountFrequency (%)
1022
 
0.1%
1032
 
0.1%
1042
 
0.1%
1052
 
0.1%
1062
 
0.1%
1072
 
0.1%
1092
 
0.1%
1116
0.2%
1152
 
0.1%
1164
0.1%
ValueCountFrequency (%)
14992
 
0.1%
14982
 
0.1%
14964
0.1%
14956
0.2%
14922
 
0.1%
14908
0.3%
14882
 
0.1%
14856
0.2%
14822
 
0.1%
14804
0.1%

Department
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Research & Development
1922 
Sales
892 
Human Resources
 
126

Length

Max length22
Median length22
Mean length16.54217687
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development1922
65.4%
Sales892
30.3%
Human Resources126
 
4.3%

Length

2022-02-27T19:11:24.915285image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:25.015901image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
research1922
27.8%
1922
27.8%
development1922
27.8%
sales892
12.9%
human126
 
1.8%
resources126
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DistanceFromHome
Real number (ℝ≥0)

Distinct29
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517007
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:25.116340image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.105485131
Coefficient of variation (CV)0.881748179
Kurtosis-0.2264931379
Mean9.192517007
Median Absolute Deviation (MAD)5
Skewness0.9576287023
Sum27026
Variance65.69888921
MonotonicityNot monotonic
2022-02-27T19:11:25.259242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2422
14.4%
1416
14.1%
10172
 
5.9%
9170
 
5.8%
3168
 
5.7%
7168
 
5.7%
8160
 
5.4%
5130
 
4.4%
4128
 
4.4%
6118
 
4.0%
Other values (19)888
30.2%
ValueCountFrequency (%)
1416
14.1%
2422
14.4%
3168
 
5.7%
4128
 
4.4%
5130
 
4.4%
6118
 
4.0%
7168
 
5.7%
8160
 
5.4%
9170
5.8%
10172
5.9%
ValueCountFrequency (%)
2954
1.8%
2846
1.6%
2724
0.8%
2650
1.7%
2550
1.7%
2456
1.9%
2354
1.8%
2238
1.3%
2136
1.2%
2050
1.7%

Education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
1144 
4
796 
2
564 
1
340 
5
 
96

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
31144
38.9%
4796
27.1%
2564
19.2%
1340
 
11.6%
596
 
3.3%

Length

2022-02-27T19:11:25.409634image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:25.506200image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
31144
38.9%
4796
27.1%
2564
19.2%
1340
 
11.6%
596
 
3.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EducationField
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Life Sciences
1212 
Medical
928 
Marketing
318 
Technical Degree
264 
Other
164 

Length

Max length16
Median length13
Mean length10.53333333
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences1212
41.2%
Medical928
31.6%
Marketing318
 
10.8%
Technical Degree264
 
9.0%
Other164
 
5.6%
Human Resources54
 
1.8%

Length

2022-02-27T19:11:25.608288image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:25.702106image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
life1212
27.1%
sciences1212
27.1%
medical928
20.8%
marketing318
 
7.1%
technical264
 
5.9%
degree264
 
5.9%
other164
 
3.7%
human54
 
1.2%
resources54
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EnvironmentSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
906 
4
892 
2
574 
1
568 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3906
30.8%
4892
30.3%
2574
19.5%
1568
19.3%

Length

2022-02-27T19:11:25.810795image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:25.898872image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
3906
30.8%
4892
30.3%
2574
19.5%
1568
19.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Male
1764 
Female
1176 

Length

Max length6
Median length4
Mean length4.8
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male1764
60.0%
Female1176
40.0%

Length

2022-02-27T19:11:25.999443image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:26.093962image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
male1764
60.0%
female1176
40.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.89115646
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:26.199064image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.32596874
Coefficient of variation (CV)0.308477948
Kurtosis-1.196405417
Mean65.89115646
Median Absolute Deviation (MAD)18
Skewness-0.03229445229
Sum193720
Variance413.1450051
MonotonicityNot monotonic
2022-02-27T19:11:26.368064image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6658
 
2.0%
9856
 
1.9%
4256
 
1.9%
4856
 
1.9%
8456
 
1.9%
5754
 
1.8%
7954
 
1.8%
9654
 
1.8%
5452
 
1.8%
5252
 
1.8%
Other values (61)2392
81.4%
ValueCountFrequency (%)
3038
1.3%
3130
1.0%
3248
1.6%
3338
1.3%
3424
0.8%
3536
1.2%
3636
1.2%
3736
1.2%
3826
0.9%
3934
1.2%
ValueCountFrequency (%)
10038
1.3%
9940
1.4%
9856
1.9%
9742
1.4%
9654
1.8%
9546
1.6%
9444
1.5%
9332
1.1%
9250
1.7%
9136
1.2%

JobInvolvement
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
1736 
2
750 
4
288 
1
 
166

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
31736
59.0%
2750
25.5%
4288
 
9.8%
1166
 
5.6%

Length

2022-02-27T19:11:26.525741image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:26.613310image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
31736
59.0%
2750
25.5%
4288
 
9.8%
1166
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobLevel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
1
1086 
2
1068 
3
436 
4
212 
5
138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11086
36.9%
21068
36.3%
3436
14.8%
4212
 
7.2%
5138
 
4.7%

Length

2022-02-27T19:11:26.703376image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:26.795653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
11086
36.9%
21068
36.3%
3436
14.8%
4212
 
7.2%
5138
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobRole
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Sales Executive
652 
Research Scientist
584 
Laboratory Technician
518 
Manufacturing Director
290 
Healthcare Representative
262 
Other values (4)
634 

Length

Max length25
Median length18
Mean length18.0707483
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowResearch Scientist
3rd rowLaboratory Technician
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive652
22.2%
Research Scientist584
19.9%
Laboratory Technician518
17.6%
Manufacturing Director290
9.9%
Healthcare Representative262
8.9%
Manager204
 
6.9%
Sales Representative166
 
5.6%
Research Director160
 
5.4%
Human Resources104
 
3.5%

Length

2022-02-27T19:11:26.906655image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:27.011081image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
sales818
14.4%
research744
13.1%
executive652
11.5%
scientist584
10.3%
laboratory518
9.1%
technician518
9.1%
director450
7.9%
representative428
7.5%
manufacturing290
 
5.1%
healthcare262
 
4.6%
Other values (3)412
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
4
918 
3
884 
1
578 
2
560 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4918
31.2%
3884
30.1%
1578
19.7%
2560
19.0%

Length

2022-02-27T19:11:27.147020image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:27.237894image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
4918
31.2%
3884
30.1%
1578
19.7%
2560
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MaritalStatus
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Married
1346 
Single
940 
Divorced
654 

Length

Max length8
Median length7
Mean length6.902721088
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married1346
45.8%
Single940
32.0%
Divorced654
22.2%

Length

2022-02-27T19:11:27.339448image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:27.445447image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
married1346
45.8%
single940
32.0%
divorced654
22.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1349
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.931293
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:27.561724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097
Q12911
median4919
Q38380
95-th percentile17856
Maximum19999
Range18990
Interquartile range (IQR)5469

Descriptive statistics

Standard deviation4707.15577
Coefficient of variation (CV)0.7238513768
Kurtosis1.001480439
Mean6502.931293
Median Absolute Deviation (MAD)2199
Skewness1.369117141
Sum19118618
Variance22157315.44
MonotonicityNot monotonic
2022-02-27T19:11:27.734166image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23428
 
0.3%
61426
 
0.2%
27416
 
0.2%
25596
 
0.2%
26106
 
0.2%
24516
 
0.2%
55626
 
0.2%
34526
 
0.2%
23806
 
0.2%
63476
 
0.2%
Other values (1339)2878
97.9%
ValueCountFrequency (%)
10092
0.1%
10512
0.1%
10522
0.1%
10812
0.1%
10912
0.1%
11022
0.1%
11182
0.1%
11292
0.1%
12002
0.1%
12232
0.1%
ValueCountFrequency (%)
199992
0.1%
199732
0.1%
199432
0.1%
199262
0.1%
198592
0.1%
198472
0.1%
198452
0.1%
198332
0.1%
197402
0.1%
197172
0.1%

MonthlyRate
Real number (ℝ≥0)

Distinct1427
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.1034
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:27.897874image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3376
Q18045
median14235.5
Q320462
95-th percentile25440
Maximum26999
Range24905
Interquartile range (IQR)12417

Descriptive statistics

Standard deviation7116.575021
Coefficient of variation (CV)0.4972069873
Kurtosis-1.214931492
Mean14313.1034
Median Absolute Deviation (MAD)6206.5
Skewness0.01856832054
Sum42080524
Variance50645640.03
MonotonicityNot monotonic
2022-02-27T19:11:28.073212image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42236
 
0.2%
91506
 
0.2%
95584
 
0.1%
128584
 
0.1%
220744
 
0.1%
253264
 
0.1%
90964
 
0.1%
130084
 
0.1%
123554
 
0.1%
77444
 
0.1%
Other values (1417)2896
98.5%
ValueCountFrequency (%)
20942
0.1%
20972
0.1%
21042
0.1%
21122
0.1%
21222
0.1%
21254
0.1%
21372
0.1%
22272
0.1%
22432
0.1%
22532
0.1%
ValueCountFrequency (%)
269992
0.1%
269972
0.1%
269682
0.1%
269592
0.1%
269562
0.1%
269332
0.1%
269142
0.1%
268972
0.1%
268942
0.1%
268622
0.1%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.693197279
Minimum0
Maximum9
Zeros394
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:28.215112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.497583994
Coefficient of variation (CV)0.927367636
Kurtosis0.008154234519
Mean2.693197279
Median Absolute Deviation (MAD)1
Skewness1.025946912
Sum7918
Variance6.237925807
MonotonicityNot monotonic
2022-02-27T19:11:28.317628image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11042
35.4%
0394
 
13.4%
3318
 
10.8%
2292
 
9.9%
4278
 
9.5%
7148
 
5.0%
6140
 
4.8%
5126
 
4.3%
9104
 
3.5%
898
 
3.3%
ValueCountFrequency (%)
0394
 
13.4%
11042
35.4%
2292
 
9.9%
3318
 
10.8%
4278
 
9.5%
5126
 
4.3%
6140
 
4.8%
7148
 
5.0%
898
 
3.3%
9104
 
3.5%
ValueCountFrequency (%)
9104
 
3.5%
898
 
3.3%
7148
 
5.0%
6140
 
4.8%
5126
 
4.3%
4278
 
9.5%
3318
 
10.8%
2292
 
9.9%
11042
35.4%
0394
 
13.4%

Over18
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
True
2940 
ValueCountFrequency (%)
True2940
100.0%
2022-02-27T19:11:28.402542image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

OverTime
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
False
2108 
True
832 
ValueCountFrequency (%)
False2108
71.7%
True832
 
28.3%
2022-02-27T19:11:28.437642image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.20952381
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:28.534420image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.659315013
Coefficient of variation (CV)0.2405936609
Kurtosis-0.3021290683
Mean15.20952381
Median Absolute Deviation (MAD)2
Skewness0.8207086405
Sum44716
Variance13.39058637
MonotonicityNot monotonic
2022-02-27T19:11:28.649734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11420
14.3%
13418
14.2%
14402
13.7%
12396
13.5%
15202
6.9%
18178
6.1%
17164
 
5.6%
16156
 
5.3%
19152
 
5.2%
22112
 
3.8%
Other values (5)340
11.6%
ValueCountFrequency (%)
11420
14.3%
12396
13.5%
13418
14.2%
14402
13.7%
15202
6.9%
16156
 
5.3%
17164
 
5.6%
18178
6.1%
19152
 
5.2%
20110
 
3.7%
ValueCountFrequency (%)
2536
 
1.2%
2442
 
1.4%
2356
 
1.9%
22112
3.8%
2196
3.3%
20110
3.7%
19152
5.2%
18178
6.1%
17164
5.6%
16156
5.3%

PerformanceRating
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
2488 
4
452 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
32488
84.6%
4452
 
15.4%

Length

2022-02-27T19:11:28.776512image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:28.856104image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
32488
84.6%
4452
 
15.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RelationshipSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
918 
4
864 
2
606 
1
552 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3918
31.2%
4864
29.4%
2606
20.6%
1552
18.8%

Length

2022-02-27T19:11:28.931922image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:29.014452image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
3918
31.2%
4864
29.4%
2606
20.6%
1552
18.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

StandardHours
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
80
2940 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
802940
100.0%

Length

2022-02-27T19:11:29.112821image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:29.193213image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
802940
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

StockOptionLevel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
0
1262 
1
1192 
2
316 
3
170 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01262
42.9%
11192
40.5%
2316
 
10.7%
3170
 
5.8%

Length

2022-02-27T19:11:29.266095image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:29.347587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
01262
42.9%
11192
40.5%
2316
 
10.7%
3170
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TotalWorkingYears
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27959184
Minimum0
Maximum40
Zeros22
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:29.453325image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.77945785
Coefficient of variation (CV)0.6896932055
Kurtosis0.9146652211
Mean11.27959184
Median Absolute Deviation (MAD)4
Skewness1.116601334
Sum33162
Variance60.51996445
MonotonicityNot monotonic
2022-02-27T19:11:29.611510image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10404
 
13.7%
6250
 
8.5%
8206
 
7.0%
9192
 
6.5%
5176
 
6.0%
7162
 
5.5%
1162
 
5.5%
4126
 
4.3%
1296
 
3.3%
384
 
2.9%
Other values (30)1082
36.8%
ValueCountFrequency (%)
022
 
0.7%
1162
5.5%
262
 
2.1%
384
 
2.9%
4126
4.3%
5176
6.0%
6250
8.5%
7162
5.5%
8206
7.0%
9192
6.5%
ValueCountFrequency (%)
404
 
0.1%
382
 
0.1%
378
0.3%
3612
0.4%
356
 
0.2%
3410
0.3%
3314
0.5%
3218
0.6%
3118
0.6%
3014
0.5%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.799319728
Minimum0
Maximum6
Zeros108
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:29.770133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.289051264
Coefficient of variation (CV)0.4604873288
Kurtosis0.4921087248
Mean2.799319728
Median Absolute Deviation (MAD)1
Skewness0.5528417007
Sum8230
Variance1.661653161
MonotonicityNot monotonic
2022-02-27T19:11:29.870125image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21094
37.2%
3982
33.4%
4246
 
8.4%
5238
 
8.1%
1142
 
4.8%
6130
 
4.4%
0108
 
3.7%
ValueCountFrequency (%)
0108
 
3.7%
1142
 
4.8%
21094
37.2%
3982
33.4%
4246
 
8.4%
5238
 
8.1%
6130
 
4.4%
ValueCountFrequency (%)
6130
 
4.4%
5238
 
8.1%
4246
 
8.4%
3982
33.4%
21094
37.2%
1142
 
4.8%
0108
 
3.7%

WorkLifeBalance
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
1786 
2
688 
4
306 
1
 
160

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
31786
60.7%
2688
 
23.4%
4306
 
10.4%
1160
 
5.4%

Length

2022-02-27T19:11:29.990950image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T19:11:30.075302image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
31786
60.7%
2688
 
23.4%
4306
 
10.4%
1160
 
5.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

YearsAtCompany
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.008163265
Minimum0
Maximum40
Zeros88
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:30.177910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.125482783
Coefficient of variation (CV)0.8740496691
Kurtosis3.926771682
Mean7.008163265
Median Absolute Deviation (MAD)3
Skewness1.763628341
Sum20604
Variance37.52153933
MonotonicityNot monotonic
2022-02-27T19:11:30.325673image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5392
13.3%
1342
11.6%
3256
8.7%
2254
8.6%
10240
8.2%
4220
 
7.5%
7180
 
6.1%
9164
 
5.6%
8160
 
5.4%
6152
 
5.2%
Other values (27)580
19.7%
ValueCountFrequency (%)
088
 
3.0%
1342
11.6%
2254
8.6%
3256
8.7%
4220
7.5%
5392
13.3%
6152
 
5.2%
7180
6.1%
8160
5.4%
9164
5.6%
ValueCountFrequency (%)
402
 
0.1%
372
 
0.1%
364
 
0.1%
342
 
0.1%
3310
0.3%
326
0.2%
316
0.2%
302
 
0.1%
294
 
0.1%
274
 
0.1%

YearsInCurrentRole
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.229251701
Minimum0
Maximum18
Zeros488
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:30.980923image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.622520593
Coefficient of variation (CV)0.8565393713
Kurtosis0.4745664051
Mean4.229251701
Median Absolute Deviation (MAD)3
Skewness0.9168946757
Sum12434
Variance13.12265545
MonotonicityNot monotonic
2022-02-27T19:11:31.108243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2744
25.3%
0488
16.6%
7444
15.1%
3270
 
9.2%
4208
 
7.1%
8178
 
6.1%
9134
 
4.6%
1114
 
3.9%
674
 
2.5%
572
 
2.4%
Other values (9)214
 
7.3%
ValueCountFrequency (%)
0488
16.6%
1114
 
3.9%
2744
25.3%
3270
 
9.2%
4208
 
7.1%
572
 
2.4%
674
 
2.5%
7444
15.1%
8178
 
6.1%
9134
 
4.6%
ValueCountFrequency (%)
184
 
0.1%
178
 
0.3%
1614
 
0.5%
1516
 
0.5%
1422
 
0.7%
1328
 
1.0%
1220
 
0.7%
1144
 
1.5%
1058
2.0%
9134
4.6%

YearsSinceLastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.187755102
Minimum0
Maximum15
Zeros1162
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:31.241466image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.221882014
Coefficient of variation (CV)1.472688607
Kurtosis3.604485231
Mean2.187755102
Median Absolute Deviation (MAD)1
Skewness1.983276643
Sum6432
Variance10.38052371
MonotonicityNot monotonic
2022-02-27T19:11:31.356235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
01162
39.5%
1714
24.3%
2318
 
10.8%
7152
 
5.2%
4122
 
4.1%
3104
 
3.5%
590
 
3.1%
664
 
2.2%
1148
 
1.6%
836
 
1.2%
Other values (6)130
 
4.4%
ValueCountFrequency (%)
01162
39.5%
1714
24.3%
2318
 
10.8%
3104
 
3.5%
4122
 
4.1%
590
 
3.1%
664
 
2.2%
7152
 
5.2%
836
 
1.2%
934
 
1.2%
ValueCountFrequency (%)
1526
 
0.9%
1418
 
0.6%
1320
 
0.7%
1220
 
0.7%
1148
 
1.6%
1012
 
0.4%
934
 
1.2%
836
 
1.2%
7152
5.2%
664
2.2%

YearsWithCurrManager
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.123129252
Minimum0
Maximum17
Zeros526
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-02-27T19:11:31.489101image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.567529037
Coefficient of variation (CV)0.8652479267
Kurtosis0.1687248825
Mean4.123129252
Median Absolute Deviation (MAD)3
Skewness0.8330253638
Sum12122
Variance12.72726343
MonotonicityNot monotonic
2022-02-27T19:11:31.611046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2688
23.4%
0526
17.9%
7432
14.7%
3284
9.7%
8214
 
7.3%
4196
 
6.7%
1152
 
5.2%
9128
 
4.4%
562
 
2.1%
658
 
2.0%
Other values (8)200
 
6.8%
ValueCountFrequency (%)
0526
17.9%
1152
 
5.2%
2688
23.4%
3284
9.7%
4196
 
6.7%
562
 
2.1%
658
 
2.0%
7432
14.7%
8214
 
7.3%
9128
 
4.4%
ValueCountFrequency (%)
1714
 
0.5%
164
 
0.1%
1510
 
0.3%
1410
 
0.3%
1328
 
1.0%
1236
 
1.2%
1144
 
1.5%
1054
 
1.8%
9128
4.4%
8214
7.3%

Interactions

2022-02-27T19:11:19.218466image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:42.723604image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:44.951045image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:46.921543image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:49.123520image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:51.554507image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:54.502052image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:56.959125image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:59.369525image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:02.205287image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:05.255488image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:08.154712image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:11.915093image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:14.421159image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:16.843929image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:19.361573image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:42.868672image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:45.082112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:47.057344image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:49.260559image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:51.737011image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:54.684261image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:57.137835image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:59.541218image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:02.403476image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:05.440249image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:08.386431image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:12.086392image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:14.613947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:16.993843image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:19.495617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:42.995439image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:45.204819image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:47.183454image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:49.386007image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:51.888292image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:54.859672image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:57.315640image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:59.700014image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:02.575848image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:05.633674image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:08.588511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:12.227990image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:14.766356image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:17.136792image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:19.635351image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:43.130735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:45.335840image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:47.320260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:49.524228image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:52.046933image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:55.032256image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:57.461157image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:59.859092image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:02.732246image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:05.846383image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:08.804492image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:12.379324image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:14.923054image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:17.276762image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:19.779747image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:43.268385image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:45.470398image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:47.455167image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:49.658649image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:52.240348image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:55.209756image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:57.620363image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:59.990337image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:02.917110image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:06.055882image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:09.020630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:12.539700image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:15.071050image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:17.426590image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:19.938101image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:43.410410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:45.604390image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:47.729174image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:49.799184image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:52.430899image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:55.370778image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:57.811653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:00.171761image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:03.121800image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:06.216369image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:09.239266image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:12.711470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:15.243032image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:17.586986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:11:20.103582image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:43.551151image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:45.739818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-27T19:10:47.869221image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

2022-02-27T19:11:31.782193image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-27T19:11:32.142107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-27T19:11:32.495717image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-27T19:11:32.846662image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-27T19:11:33.131761image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-27T19:11:22.068938image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-27T19:11:23.222272image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

EmployeeNumberAttritionAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
01Yes41Travel_Rarely1102Sales12Life Sciences2Female9432Sales Executive4Single5993194798YYes11318008016405
12No49Travel_Frequently279Research & Development81Life Sciences3Male6122Research Scientist2Married5130249071YNo2344801103310717
23Yes37Travel_Rarely1373Research & Development22Other4Male9221Laboratory Technician3Single209023966YYes15328007330000
34No33Travel_Frequently1392Research & Development34Life Sciences4Female5631Research Scientist3Married2909231591YYes11338008338730
45No27Travel_Rarely591Research & Development21Medical1Male4031Laboratory Technician2Married3468166329YNo12348016332222
56No32Travel_Frequently1005Research & Development22Life Sciences4Male7931Laboratory Technician4Single3068118640YNo13338008227736
67No59Travel_Rarely1324Research & Development33Medical3Female8141Laboratory Technician1Married267099644YYes204180312321000
78No30Travel_Rarely1358Research & Development241Life Sciences4Male6731Laboratory Technician3Divorced2693133351YNo22428011231000
89No38Travel_Frequently216Research & Development233Life Sciences4Male4423Manufacturing Director3Single952687870YNo214280010239718
910No36Travel_Rarely1299Research & Development273Medical3Male9432Healthcare Representative3Married5237165776YNo133280217327777

Last rows

EmployeeNumberAttritionAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
29302931No29Travel_Rarely468Research & Development284Medical4Female7321Research Scientist1Single378584891YNo14328005315404
29312932Yes50Travel_Rarely410Sales283Marketing4Male3923Sales Executive1Divorced10854165864YYes133280120333220
29322933No39Travel_Rarely722Sales241Marketing2Female6024Sales Executive4Married1203188280YNo1131801212220996
29332934No31Non-Travel325Research & Development53Medical2Male7432Manufacturing Director1Single993637870YNo193280010239417
29342935No26Travel_Rarely1167Sales53Other4Female3021Sales Representative3Single2966213780YNo18348005234200
29352936No36Travel_Frequently884Research & Development232Medical3Male4142Laboratory Technician4Married2571122904YNo173380117335203
29362937No39Travel_Rarely613Research & Development61Medical4Male4223Healthcare Representative1Married9991214574YNo15318019537717
29372938No27Travel_Rarely155Research & Development43Life Sciences2Male8742Manufacturing Director2Married614251741YYes20428016036203
29382939No49Travel_Frequently1023Sales23Medical4Male6322Sales Executive2Married5390132432YNo143480017329608
29392940No34Travel_Rarely628Research & Development83Medical2Male8242Laboratory Technician3Married4404102282YNo12318006344312